A phasor measurement unit (PMU) is a device which measures the 50/60 Hz AC waveform (voltages and currents) on an electricity grid using a common time source Global Positioning System (GPS) radio clock for synchronization. Time synchronization allows synchronized real-time measurements of multiple remote measurement points on the grid. The resulting measurement is known as a synchrophasor. The obtained synchrophasor measurement is of great importance in tremendous modern power system applications, for instance, support demand response mechanism to manage a power system, detect early fault allowing for isolation of operative system and prevent power outages, provide trustful information for power system state estimation, etc. Phasor measurement unit (PMU) is envisioned to be one of the enabling technologies in smart grid, with the promise of massive installation in the future power systems. On one hand, most synchrophasor-based applications, especially the mission critical ones, require the measurements to be very reliable and accurate. On the other hand, although PMU data are expected to be highly accurate, this potential accuracy and reliability are not always achieved in actual field installation due to various causes [1]. It has been observed under many occasions that PMU measurements can have various types of data quality issues. To ensure accurate, reliable and consistent PMU data, there are pressing needs to calibrate PMU to fulfill the claimed performance.
As discussed in [2], the PMU device itself is typically very accurate, but the instrumental channel, where PMU gets its inputs, is usually much less accurate. Specifically, the instrumentation channel (e.g., potential and current transformers) can introduce magnitude and phase angle errors that can be magnitudes of orders higher than the typical PMU accuracy. A practically useful calibration method should be capable of handling inaccuracies originated from both PMU and its instrumentation channel.
Previously the Performance and Standards Task Team (PSTT) published a PMU system testing and calibration guide [3]. As discussed and widely accepted in the 2016 NASPI Work Group meeting, PMU data quality efforts need to be implemented to ensure the highest synchrophasor signal quality for applications. The modified IEEE C37.118 standard requires the total vector error (TVE) between a measured phasor and its true value to be well within 1% under steady-state operating conditions [4]. Towards these requirements, many PMU calibration schemes have been proposed. In general, these methods can be divided into two categories based on how they are implemented: offline testing/calibration [5-11] and model-based approaches [12-16].
The former works by comparing PMU output against standard testing signal(s), using certain types of specialized equipment or systems whose accuracies are at least one level greater than the to-be-tested PMUs. This type of methods requires specialized expensive equipment/system, and due to their offline nature, errors originated from the instrumentation channel cannot be duplicated and compensated.
The latter works by fitting PMU measurements into a mathematical model for fidelity check, assuming parameters of the system/device(s) and the model are known a priori and accurate. In [12], the authors present a phasor-data-based state estimator (PSE) that is capable of identifying and correcting bias error(s) in phase angles. This approach assumes the phasor magnitudes and network parameters are both accurate. One paper in [13] proposes the idea of a “super calibrator” for substation-level data filtering and state estimation, the input of which includes PMU data, SCADA data, and a detailed 3-phase model of the substation, etc. Despite complexity of the model, the accuracy level of SCADA data adds uncertainty, or even degrades performance of the approach. Paper [1] proposes a calibration-factor-based iterative non-linear solution approach for 3-phase PMU data calibration. Performance of the approach is highly dependent upon accuracy of the 3-phase transmission line parameters in the EMS database. The PMU data calibration approach in [14] again assumes the transmission line (TL) impedances are known to be exact. Papers [15] and [16] attempt to accomplish line parameter estimation and PMU calibration simultaneously, with the assumption that one of the two PMUs generates perfect measurements, which, in practice, is really difficult to tell. The strong assumptions used in existing model-based methods undermine their practicability.
This invention presents a novel data mining based synchrophasor measurement calibration framework which detects and corrects the overall systematic or bias error(s) introduced by both PMU and its instrumentation channel. Major contribution of the proposed method lies in that it does not require accurate prior knowledge of the system mathematical model/parameters. Furthermore, one byproduct of the proposed method is more accurate impedance parameters of the transmission line for EMS database and protective relay settings. By relaxing those strong assumptions employed in existing model based approaches, the proposed method advances the practicability of online PMU calibration.
The remainder of this patent application is organized as follows. Section I describes the problem and related mathematical models. Section II presents the proposed framework. Case studies are presented in section III while conclusion and advantages are discussed in section IV.